@InProceedings{AntonioHernandez2011,
author="Antonio Hernandez
and Carlo Gatta
and Sergio Escalera
and Laura Igual
and Victoria Martin Yuste
and Petia Radeva",
title="Accurate and Robust Fully-Automatic QCA: Method and Numerical  Validation",
booktitle="14th International Conference on Medical Image Computing and Computer Assisted Intervention",
year="2011",
publisher="Springer",
volume="14",
number="3",
pages="496--503",
abstract="The Quantitative Coronary Angiography (QCA) is a methodology used to evaluate the arterial diseases and, in particular, the degree of stenosis. In this paper we propose AQCA, a fully automatic method for vessel segmentation based on graph cut theory. Vesselness, geodesic paths and a new multi-scale edgeness map are used to compute a globally optimal artery segmentation. We evaluate the method performance in a rigorous numerical way on two datasets. The method can detect an artery with precision 92.9 +/- 5\% and sensitivity 94.2 +/- 6\%. The average absolute distance error between detected and ground truth centerline is 1.13 +/- 0.11 pixels (about 0.27 +/- 0.025 mm) and the absolute relative error in the vessel caliber estimation is 2.93\% with almost no bias. Moreover, the method can discriminate between arteries and catheter with an accuracy of 96.4\%.",
optnote="MILAB;HuPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1769), last updated on Fri, 07 Mar 2014 10:49:51 +0100",
isbn="978-3-642-23625-9"
}